How Appreciation and Attention Affect Contributions to Electronic Networks of Practice

ABSTRACTWe conducted three studies to examine how two types of user-generated feedback, appreciation and attention, affect users' decisions to make voluntary knowledge contributions to electronic networks of practice (ENPs). Appreciation is reflected in positive ratings, votes, and helpfulness evaluations. Attention is reflected in the number of views of contributed content. The first study used clickstream data from a college application ENP in China, where information seekers can read posted information asynchronously and request synchronous consultations with volunteers. The second study was a controlled online experiment in the United States where we assessed users' willingness to answer questions in a college application ENP asynchronously. The third study examined knowledge contribution across a diverse set of topics using a well-established ENP that serves more than 100 countries. In all three studies, the results consistently show that greater appreciation increased continued knowledge contribution, but greater attention without sufficient appreciation negatively affected contributions. Our findings show the theoretically intertwined nature of attention and appreciation and offer insights for the design and management of ENP feedback systems to encourage user contributions.KEYWORDS: Electronic networks of practiceENPonline communitiesonline feedbackincentive designonline knowledge contributiononline volunteerism Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2022.2127443Disclosure StatementNo potential conflict of interest was reported by the authors.Notes1 Senior high school students have a time window of about four days after they learn their national exam scores to complete their primary round of college applications. Students submit a list of colleges and programs with strict order of preference. Colleges make admission decisions, taking into consideration how students rank them in their orders of preference. If not admitted, students submit their secondary round of applications for lower-tier colleges about a month later. Such a sequential admissions process presents a complex decision for students because they need to balance the trade-off between risk and quality of admission.2 We interpreted the coefficient estimates this way since we have standardized both NumRating and NumView after adding one and taking logarithms. The average level of appreciation is 0.426, and the standard deviation for NumRatingit, the log-transformed appreciation feedback, is 0.702. A one standard deviation increase in NumRatingit corresponds to an increase from 1.158 five-star ratings (the original scale) to exp(0.702 + 0.426) -1 = 2.09 five-star ratings. The results for the non-standardized variables with more direct interpretations are included in the Online Supplemental Appendix. Note that the first-order terms for non-standardized variables cannot be interpreted directly when the interaction term is included.3 These calculations are based on the summary statistics in the variables. The average level of appreciation is 1.158 in its original scale, and the standard deviation for NumRatingit, the log-transformed appreciation feedback, is 0.702.4 The platform also allows a downvote feedback, but this feedback is not visible to knowledge contributors because the Q&A platform does not want its users to focus on negativity. The downvotes are used to alter the visibility of questions or answers. As such, we do not consider it as feedback to the contributors.5 This observation window is sufficient to capture the impact of feedback on knowledge contribution for three reasons. First, the identification of our model relies on within-subject variation in the feedback, and we observe great variation in both attention and appreciation within this four-day period. Specifically, the most views were generated on Day 2, and the most upvotes were generated on Day 3. Second, we examine the short-term impact of feedback on knowledge contribution on the next day. Third, the limitation of a relatively short panel can be addressed by our relatively large number of users.Additional informationNotes on contributorsXue (Jane) TanXue (Jane) Tan (janetan@iu.edu) is an assistant professor in the Department of Operations and Decision Technologies, Kelley School of Business, Indiana University. She received her Ph.D. in Business Administration from the Foster School of Business, University of Washington. Dr. Tan's research interests include crowdfunding, social media fundraising, online volunteerism, and electronic commerce. She has published in Information Systems Research and MIS Quarterly.Fujie JinFujie Jin (jinf@indiana.edu) is an assistant professor in the Operations & Decisions Technologies Department at Kelley School of Business, Indiana University. She holds a Ph.D. in Operations and Information Systems from the Wharton School, University of Pennsylvania. Dr. Jin's research focuses on the impact of technology on organizations and user behavior on platforms. Her work has been published in leading journals and conference proceedings in.Alan R. DennisAlan R. Dennis (ardennis@indiana.edu; corresponding author) is Professor of Information Systems and holds the John T. Chambers Chair of Internet Systems in the Kelley School of Business at Indiana University. He has written more than 150 research papers, and has won numerous awards for his theoretical and applied research. Dr. Dennis's research focuses on team collaboration, fake news on social media, AI-controlled digital humans, and information security. He is a Fellow of Association for Information Systems and received the LEO Award in 2021. He is also a Past President of AIS.